| Name | |
|---|---|
| Julia Schönthal | julia.schoenthal@students.fhnw.ch |
| Coraline Meyer | coraline.meyer@students.fhnw.ch |
| Laura Roggo | laura.roggo@students.fhnw.ch |
| Aleksandra Grzegorczyk | aleksandra.grzegorczyk@students.fhnw.ch |
Andreas Martin
Charuta Pande
Devid Montecchiari
This project focuses on the digitalisation and automation of an employee recruitment process. The goal is to analyse the existing AS-IS process, identify improvement opportunities, and design a more efficient TO-BE process using BPMN, DMN, Camunda and Make.
In the AS-IS process, the recruitment workflow covers several steps, starting with the identification of a hiring need and ending with the preparation and sending of employment documents. Many activities are performed manually by HR, such as checking applications, communicating with candidates, scheduling interviews and preparing documents. This creates administrative workload and increases the risk of delays or inconsistent decisions.
The TO-BE process aims to simplify and improve this workflow by introducing automation, decision support and service integration. Selected process steps are automated through Make scenarios, while decision logic is supported by DMN tables. Camunda is used to orchestrate the workflow and manage the interaction between manual user tasks and automated service tasks.
The main objective of the project is not only to model the process, but also to demonstrate how digitalisation can reduce repetitive manual work, improve process transparency and create a more scalable recruitment process. The implemented solution should show how BPMN, DMN and service automation can work together in a practical hiring scenario.
The project focuses on the recruitment process and especially on the digitalisation of selected steps within the candidate application and evaluation journey.
Included in the scope are:
- modelling the AS-IS recruitment process
- identifying weaknesses and automation opportunities
- designing a TO-BE process
- automating selected process steps with Make
- using DMN for candidate-related decisions
- collecting and storing structured candidate data
- integrating Google Forms and Google Sheets
- demonstrating the workflow in Camunda
The following areas are outside the scope of this project:
- onboarding
- payroll
- long-term employee management
- posting of the jobs
The recruitment process is modeled in BPMN and covers the main steps from job posting to candidate evaluation. It includes:
- The hiring process starts when a department manager identifies a hiring need and creates a job requisition.
- HR reviews the requisition; if it is not approved, the process ends with rejection. If approved, HR publishes the job posting and stores the posting details in the database.
- Candidates submit applications, which HR receives and reviews for compliance.
- Non-compliant applications are rejected, while compliant applications are reviewed further by the department manager.
- The department manager decides whether the application should proceed. Approved candidates are sent to HR for interview approval and scheduling; rejected candidates are notified.
- HR schedules and conducts interviews, then decides whether to employ the candidate.
- If the candidate is rejected after interview, a rejection notification is sent. If selected, HR sends an employment offer.
- After the candidate responds to the offer, HR checks whether the offer is accepted. If not, the candidate is rejected.
- Once the offer is accepted, HR prepares employment documents, including the employment contract and other scanned documents.
- The process ends when the employment documents are sent to the candidate.
The goal is to clearly structure the process and reduce manual effort by automating repetitive tasks and decisions where possible.
During the analysis of the as-is recruitment process, several weaknesses and improvement opportunities were identified.
Main challenges:
- Many HR activities are performed manually
- Candidate screening depends heavily on human evaluation
- Repetitive tasks increase administrative workload
- Communication with candidates can be delayed
- The process is long and difficult to maintain
- The workflow is tightly connected in one large process
- Reusing process parts for other recruitment scenarios is difficult
- Manual document preparation requires significant effort
- Human errors and inconsistent decisions may occur
Identified improvement opportunities
The analysis showed that several activities could benefit from automation and process simplification.
Potential improvements include:
- Automated candidate screening
- Faster and more standardized communication with candidates
- Better separation between process steps
- Reusable process structures
- Reduced manual workload for HR employees
- Improved scalability for larger numbers of applications
- Better transparency and consistency in decision-making
- Increased use of digital data and automated workflows
These findings served as the basis for the design of the TO-BE process.
Compared with the AS-IS process, the TO-BE process introduces several improvements focused on simplification, automation and better process modularity.
In the AS-IS process, many activities were handled manually by HR employees and combined in one large end-to-end workflow. This created high administrative effort and made the process difficult to maintain and scale.
The redesigned TO-BE process simplifies the recruitment workflow by introducing automated candidate screening, automated communication, digital assessments, structured interview evaluation and semi-automated contract preparation.
Several repetitive HR activities were replaced or supported by service tasks implemented through Camunda, DMN and Make integrations.
Instead of modelling the entire recruitment lifecycle in one monolithic process, the workflow was separated into smaller and more modular process parts that can later be reused or extended for different recruitment scenarios.
Automated service tasks are highlighted in green within the BPMN models.
Overall, the TO-BE process reduces manual HR effort, improves consistency in candidate evaluation and creates a more scalable and maintainable recruitment workflow.
The TO-BE recruitment workflow starts with a digital application form implemented through Google Forms.
Candidates submit their application data directly through the online form. The submitted information is automatically stored in Google Sheets, which acts as the central data source for the workflow.
Online Application Form
The application form collects structured candidate information required for the recruitment process. This includes:
- full name
- email address
- phone number
- highest education level
- work permit status
- English level
- German level
- digital skills
- motivation text
- CV upload
Make Scenario – Application Intake and Process Trigger
A Make scenario continuously monitors Google Sheets for newly submitted applications. Once a new application is detected, Make automatically triggers a new Camunda process instance through an HTTP request.
This integration allows candidate applications to directly enter the workflow without manual HR interaction.
Improvements
- Fully digital application intake
- Automatic process triggering
- Structured candidate data collection
- Reduced manual HR interaction
- Faster workflow initiation
- Improved scalability
One of the main improvements in the TO-BE process is the automation of the initial candidate screening using a Camunda DMN decision table.
After a candidate application enters the workflow, the submitted data is automatically evaluated against predefined recruitment criteria.
DMN Decision Table
The decision table filters candidates based on predefined requirements for the position. For example:
- candidates without a valid work permit are automatically rejected
- candidates with only a high school degree are rejected
- candidates with insufficient language skills are rejected
- candidates fulfilling all required criteria proceed to the next step
The DMN table uses a unique hit policy and returns either “accepted” or “rejected” as the decision result.
This automation allows the process to eliminate unsuitable candidates before HR or department managers need to manually review applications.
Compared with the AS-IS process, this significantly reduces repetitive screening work and creates a more standardized and scalable evaluation process.
Improvements
- Reduced manual screening effort
- Faster pre-selection of candidates
- Standardized evaluation criteria
- Lower risk of inconsistent decisions
- Improved scalability
- Better use of structured digital data
After the initial application screening, candidates who do not fulfil the minimum requirements are automatically rejected through the workflow.
The rejection can happen at two different stages of the process:
- directly after the automated DMN screening
- after the department manager reviews the candidate dossier
If a candidate does not fulfil the predefined minimum requirements, such as having no valid work permit or insufficient qualifications, the workflow automatically triggers a rejection email.
Candidates can also be rejected after the manual dossier review by the department manager. This allows the process to combine automated pre-selection with human decision-making for the final candidate selection.
BPMN Rejection Flow
The rejection emails are automated through a Make scenario connected to Camunda. Once the workflow reaches the rejection service task, Camunda sends the task information to Make through an HTTP request. Make then automatically sends the rejection email to the candidate via Gmail before confirming task completion back to Camunda.
Make Scenario – Rejection Email Automation
Once the workflow reaches the rejection service task, Camunda sends the task information to Make through an HTTP request. Make then automatically sends the rejection email to the candidate via Gmail before confirming task completion back to Camunda.
Improvements
- Automated candidate communication
- Faster rejection notifications
- Reduced manual HR workload
- Standardized communication process
- Combination of automated and human decision-making
Another important improvement introduced in the TO-BE process is the integration of an automated online assessment process.
In the AS-IS process, no assessment step existed. Candidate selection mainly depended on manual dossier reviews and interviews. The TO-BE process introduces an additional evaluation step to better assess logical thinking and problem-solving skills before candidates proceed further in the recruitment workflow.
Candidates who successfully pass both the automated DMN screening and the department manager review automatically receive an assessment invitation.
BPMN Assessment Flow
The assessment invitation is automated through a Make scenario connected to Camunda.
Make Scenario – Assessment Invitation
Camunda sends the external service task information through an HTTP request, while Make processes the request and automatically sends an email containing the assessment link to the selected candidate via Gmail.
The assessment itself is implemented through Google Forms and contains multiple-choice questions designed to evaluate logical reasoning, analytical thinking and problem-solving capabilities.
Online Assessment Form
Examples of assessment questions include:
- logical number sequences
- percentage and calculation questions
- process optimization scenarios
- analytical reasoning questions
After candidates complete the assessment, the responses are automatically stored in Google Sheets. The assessment score is calculated directly within the spreadsheet based on the candidate’s answers.
A Make scenario then automatically evaluates the assessment results and routes candidates depending on their final score.
Make Scenario – Automated Assessment Evaluation
Candidates who achieve a score of 4 points or higher automatically proceed to the next recruitment step and receive an interview invitation.
Candidates with fewer than 4 points are automatically rejected and receive a rejection email through the workflow.
The Make scenario uses a router to separate successful and unsuccessful candidates based on the calculated score.
Compared with the AS-IS process, this additional assessment step improves the quality of candidate pre-selection and introduces a more objective and standardized evaluation process.
Improvements
- Introduction of structured candidate assessments
- Better evaluation of logical thinking skills
- Automated assessment evaluation
- Reduced manual review effort
- More objective candidate selection
- Faster candidate processing
- Improved scalability
- Better use of structured digital assessment data
Candidates who complete the assessment are automatically evaluated through a Make scenario connected to Google Sheets.
Assessment Evaluation Workflow
The submitted assessment answers are automatically stored and evaluated in Google Sheets. Each correct answer contributes to the candidate’s final score.
The Make scenario automatically checks the calculated result and separates candidates into two groups:
- candidates with a score lower than 4 points
- candidates with a score of 4 points or higher
Candidates who do not reach the minimum required score automatically receive a rejection email.
Candidates who successfully pass the assessment automatically receive an invitation email containing a Doodle booking link where they can select an interview slot independently.
Improvements
- Automated assessment evaluation
- Faster candidate communication
- Reduced manual screening effort
- Standardized assessment scoring
- Automated interview invitation process
Candidates who successfully pass the assessment phase proceed to the interview process.
BPMN Sub-process Interview Process
The interview workflow is implemented as a batch-oriented multi-instance subprocess. After candidates complete the online assessment, the process waits until the predefined assessment period has ended and all assessment responses have been collected. In a real-world implementation, the assessment evaluation would normally be triggered by a timer start event with a predefined evaluation date, ensuring that all candidates have sufficient time to complete the assessment before the results are processed. However, during implementation and testing, a normal start event was temporarily used instead of a timer start event, because the timer configuration would not have allowed the process to be triggered immediately for testing purposes.
The batch process is initiated through a service task that retrieves the number of qualified candidates from the same Make scenario. This information is then passed back to Camunda and used to configure the multi-instance subprocess. As a result, the interview subprocess is automatically instantiated once for each candidate who successfully passed the assessment phase.
Inside the interview subprocess, an intermediate timer event is used before the interview phase begins. The purpose of this timer is to wait until the interview booking period has ended and candidates have selected their preferred interview slots. For testing purposes, the timer duration was temporarily configured to one second in order to validate the process flow without long waiting times. In a production scenario, this timer would instead wait until a predefined booking deadline or specific date before continuing with the interview activities.
After booking a time slot through Doodle, candidates participate in a joint interview conducted by HR and the department manager.
Interview Evaluation
During the interview, candidate evaluations are directly entered into Camunda. Interviewers can document feedback, interview notes and the evaluation decision during or immediately after the interview.
The interview results are automatically transferred into Google Sheets.
Make Scenario – Save Interview Evaluation
The interview evaluation data is automatically transferred and stored through the Make integration connected to Camunda.
Candidates who are considered suitable are shortlisted for the final hiring decision.
Rejected candidates are intentionally contacted personally by phone instead of receiving an automated rejection email. This decision was made to maintain a more human-centered candidate experience, since candidates already invested significant effort by completing the assessment and interview process.
After all interviews are completed, the department manager selects one final candidate from the shortlisted candidates.
Improvements
- Structured batch interview coordination
- Reduced scheduling effort
- Centralized interview evaluation
- Better comparability between candidates
- Combination of automation and human evaluation
- Human-centered rejection communication
- More transparent final candidate selection
After the interview phase, the selected candidate proceeds to the contract preparation process.
BPMN Contract Preparation Process
Once the candidate is accepted, the workflow automatically sends an employee information form. The form is implemented using Google Forms and collects the information required for employment preparation and payroll processing.
Examples of collected information include:
- full name
- residential address
- personal email address
- personal identification number
- bank information for salary payments
- consent for personal data processing
Make Scenario – Send Employee Information Form
The employee information form is automatically sent to the selected candidate through a Make scenario connected to Camunda.
Employee Information Form
After submission, the employee information is automatically transferred into the workflow.
Make Scenario – Employee Information Submission
A Make scenario monitors the submitted employee information and automatically transfers the collected data back into Camunda through an HTTP request.
The submitted information is stored digitally in Excel sheet and can later be reused for contract preparation and other HR-related documents.
Make Scenario – Generate Employment Contract
The employment contract generation is partially automated using Google Docs templates. Candidate information collected through the workflow in Excel sheet is automatically inserted into the contract template stored in Google drive. Each time new contract is generated based on the template, new document is crated containg candidate data from the form and saved in the same folder with the person name and surname as the file name.
HR employees still review the final contract, before sending it to the employee, ensuring correctness and maintaining human oversight where necessary. Therefore it's a user task in the BPMN process, as information like role, salary, starting date may wary for a specific contract.
Make Scenario – Send Contract
Once reviewed, link to the final employment contract is automatically sent to the employee via email. Here our process ends.
Improvements
- Reduced manual document preparation
- Reuse of structured candidate data
- Faster contract preparation
- Automated document generation
- More efficient information collection
- Combination of automation and human review
- Improved process scalability
The TO-BE process integrates multiple digital services and automation tools to support workflow execution, communication and data processing.
Camunda is used as the central workflow orchestration platform. It manages the BPMN workflow, user tasks and automated service tasks throughout the recruitment process.
DMN decision tables are used for automated candidate pre-screening and support standardized decision-making based on predefined criteria.
Make is used as the integration and automation platform. It connects Camunda with external services such as Gmail, Google Forms, Google Sheets, Google Docs and Doodle through HTTP requests and automated workflows.
Integrated Technologies
| Technology | Purpose |
|---|---|
| Camunda | Workflow orchestration |
| BPMN | Process modelling |
| DMN | Automated candidate screening |
| Make | Service integration and automation |
| Google Forms | Data collection |
| Google Sheets | Assessment evaluation and data storage |
| Google Docs | Automated contract generation |
| Gmail | Automated email communication |
| Doodle | Interview scheduling |
This project demonstrated how a traditional recruitment workflow can be improved through process digitalisation, automation and workflow orchestration.
The AS-IS process contained several manual and repetitive activities, including candidate screening, communication, assessment handling and document preparation. These activities created administrative workload and made the process difficult to scale and maintain.
The redesigned TO-BE process introduced automation through Camunda, DMN and Make integrations. Candidate applications are now pre-screened automatically using decision tables, communication tasks are handled through automated email workflows, and assessment evaluations are processed digitally through Google Forms and Google Sheets.
At the same time, the project intentionally preserved human interaction in important process steps such as interviews and final candidate communication. This created a balance between automation and human-centered decision-making.
Overall, the project successfully demonstrates how BPMN modelling, decision automation and service integration can improve efficiency, consistency and scalability within a recruitment process while still maintaining a positive candidate experience.